Toxicogenomics focuses on assessing the safety of compounds using gene expression profiles. Gene expression signatures from large toxicogenomics databases are expected to perform better than small databases in identifying biomarkers for the prediction and evaluation of drug safety based on a compound's toxicological mechanisms in animal target organs. Over the past 10 years, the Japanese Toxicogenomics Project consortium (TGP) has been developing a large-scale toxicogenomics database consisting of data from 170 compounds (mostly drugs) with the aim of improving and enhancing drug safety assessment. Most of the data generated by the project (e.g. gene expression, pathology, lot number) are freely available to the public via Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System). Here, we provide a comprehensive overview of the database, including both gene expression data and metadata, with a description of experimental conditions and procedures used to generate the database. Open TG-GATEs is available from http://toxico.nibio.go.jp/english/index.html.

Influenza viruses are enveloped, negative stranded, segmented RNA viruses belonging to Orthomyxoviridae family. Each virion consists of three major subviral components, namely (i) a viral envelope decorated with three transmembrane proteins hemagglutinin (HA), neuraminidase (NA) and M2, (ii) an intermediate layer of matrix protein (M1), and (iii) an innermost helical viral ribonucleocapsid [vRNP] core formed by nucleoprotein (NP) and negative strand viral RNA (vRNA). Since complete virus particles are not found inside the cell, the processes of assembly, morphogenesis, budding and release of progeny virus particles at the plasma membrane of the infected cells are critically important for the production of infectious virions and pathogenesis of influenza viruses as well. Morphogenesis and budding require that all virus components must be brought to the budding site which is the apical plasma membrane in polarized epithelial cells whether in vitro cultured cells or in vivo infected animals. HA and NA forming the outer spikes on the viral envelope possess apical sorting signals and use exocytic pathways and lipid rafts for cell surface transport and apical sorting. NP also has apical determinant(s) and is probably transported to the apical budding site similarly via lipid rafts and/or through cortical actin microfilaments. M1 binds the NP and the exposed RNAs of vRNPs, as well as to the cytoplasmic tails (CT) and transmembrane (TM) domains of HA, NA and M2, and is likely brought to the budding site on the piggy-back of vRNP and transmembrane proteins. Budding processes involve bud initiation, bud growth and bud release. Presence of lipid rafts and assembly of viral components at the budding site can cause asymmetry of lipid bilayers and outward membrane bending leading to bud initiation and bud growth. Bud release requires fusion of the apposing viral and cellular membranes and scission of the virus buds from the infected cellular membrane. The processes involved in bud initiation, bud growth and bud scission/release require involvement both viral and host components and can affect bud closing and virus release in both positive and negative ways. Among the viral components, M1, M2 and NA play important roles in bud release and M1, M2 and NA mutations all affect the morphology of buds and released viruses. Disassembly of host cortical actin microfilaments at the pinching-off site appears to facilitate bud fission and release. Bud scission is energy dependent and only a small fraction of virus buds present on the cell surface is released. Discontinuity of M1 layer underneath the lipid bilayer, absence of outer membrane spikes, absence of lipid rafts in the lipid bilayer, as well as possible presence of M2 and disassembly of cortical actin microfilaments at the pinching off site appear to facilitate bud fission and bud release. We provide our current understanding of these important processes leading to the production of infectious influenza virus particles.

Biotechnology advances have provided novel methods for the risk assessment of chemicals. The application of microarray technologies to toxicology, known as toxicogenomics, is becoming an accepted approach for identifying chemicals with potential safety problems. Gene expression profiling is expected to identify the mechanisms that underlie the potential toxicity of chemicals. This technology has also been applied to identify biomarkers of toxicity to predict potential hazardous chemicals. Ultimately, toxicogenomics is expected to aid in risk assessment. The following discussion explores potential applications and features of the Japanese Toxicogenomics Project.

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

In chronic kidney disease (CKD), progressive nephron loss causes glomerular sclerosis, as well as tubulointerstitial fibrosis and progressive tubular injury. In this study, we aimed to identify molecular changes that reflected the histopathological progression of renal tubulointerstitial fibrosis and tubular cell damage. A discovery set of renal biopsies were obtained from 48 patients with histopathologically confirmed CKD, and gene expression profiles were determined by microarray analysis. The results indicated that hepatitis A virus cellular receptor 1 (also known as Kidney Injury Molecule-1, KIM-1), lipocalin 2 (also known as neutrophil gelatinase-associated lipocalin, NGAL), SRY-box 9, WAP four-disulfide core domain 2, and NK6 homeobox 2 were differentially expressed in CKD. Their expression levels correlated with the extent of tubulointerstitial fibrosis and tubular cell injury, determined by histopathological examination. The expression of these 5 genes was also increased as kidney damage progressed in a rodent unilateral ureteral obstruction model of CKD. We calculated a molecular score using the microarray gene expression profiles of the biopsy specimens. The composite area under the receiver operating characteristics curve plotted using this molecular score showed a high accuracy for diagnosing tubulointerstitial fibrosis and tubular cell damage. The robust sensitivity of this score was confirmed in a validation set of 5 individuals with CKD. These findings identified novel molecular markers with the potential to contribute to the detection of tubular cell damage and tubulointerstitial fibrosis in the kidney.